U.S. patent number 7,400,274 [Application Number 11/685,733] was granted by the patent office on 2008-07-15 for system and method for data feed acceleration and encryption.
This patent grant is currently assigned to Realtime Data LLC. Invention is credited to James J Fallon, Stephen J McErlain, Carlton W. Melone, Paul F. Pickel.
United States Patent |
7,400,274 |
Fallon , et al. |
July 15, 2008 |
**Please see images for:
( Reexamination Certificate ) ** |
System and method for data feed acceleration and encryption
Abstract
The transmission of broadcast data, such as financial data and
news feeds, is accelerated over a communication channel using data
compression and decompression to provide secure transmission and
transparent multiplication of communication bandwidth, as well as
reduce latency. Broadcast data may include packets having fields.
Encoders associated with particular fields may be selected to
compress those particular fields.
Inventors: |
Fallon; James J (Armonk,
NY), Pickel; Paul F. (Bethpage, NY), McErlain; Stephen
J (New York, NY), Melone; Carlton W. (Ridgewood,
NJ) |
Assignee: |
Realtime Data LLC (New York,
NY)
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Family
ID: |
38286712 |
Appl.
No.: |
11/685,733 |
Filed: |
March 13, 2007 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20070174209 A1 |
Jul 26, 2007 |
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Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
Issue Date |
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10434305 |
May 7, 2003 |
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09969987 |
Oct 3, 2001 |
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60378517 |
May 7, 2002 |
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60237571 |
Oct 3, 2000 |
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Current U.S.
Class: |
341/51;
341/50 |
Current CPC
Class: |
G06Q
40/04 (20130101); G06Q 50/188 (20130101); H04L
12/1895 (20130101); H04L 69/04 (20130101); H04L
65/607 (20130101); H04L 65/80 (20130101); H04L
67/26 (20130101); H04L 29/06027 (20130101) |
Current International
Class: |
H03M
7/34 (20060101) |
Field of
Search: |
;341/51,50 ;370/355
;380/212 ;709/238 ;358/400 ;345/419 ;714/752 |
References Cited
[Referenced By]
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Primary Examiner: Jeanglaude; Jean B
Attorney, Agent or Firm: Ropes & Gray LLP
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a continuation of patent application Ser. No.
10/434,305, filed on May 7, 2003, which is a Continuation-in-Part
of U.S. patent application Ser. No. 09/969,987, filed on Oct. 3,
2001, which claims the benefit of U.S. Provisional Application No.
60/237,571, filed on Oct. 3, 2000, each of which are fully
incorporated herein by reference. In addition, this application
claims the benefit of U.S. Provisional Application No. 60/378,517,
filed on May 7, 2002, which is fully incorporated herein by
reference.
Claims
What is claimed is:
1. A method comprising: recognizing a data field type of a data
field in a packet; selecting an encoder associated with the
recognized data field type; compressing the data in the data field
with the selected encoder; broadcasting the compressed data to a
plurality of clients, wherein the compressing achieves a
compression ratio of over 10:1; and utilizing a decompression state
machine that comprises a plurality of decompression tables to
decompress the compressed data on at least one of the plurality of
clients.
2. The method of claim 1, wherein the time of the compressing and
broadcasting is less than the time to broadcast the data in
uncompressed form.
3. The method of claim 1, wherein the time of the compressing,
broadcasting, and decompressing is less than the time to broadcast
the data in uncompressed form.
4. The method of claim 1, wherein the packet includes multiple
messages.
5. The method of claim 1, wherein the compressed data is
broadcasted as part of a financial data feed.
6. The method of claim 1, wherein the data field includes stock
information.
7. The method of claim 1, wherein the data field includes options
information.
8. The method of claim 1, wherein the compressing is lossless.
9. The method of claim 1, wherein the broadcasting utilizes
UDP.
10. The method of claim 1, wherein the compressing is performed on
a server and the compressed data is broadcast from the server.
11. A method comprising: recognizing a data field type of a data
field in a packet; selecting an encoder associated with the
recognized data field type; compressing the data in the data field
with the selected encoder; and point-to-point transmitting the
compressed data to a client, wherein the compressing utilizes a
state machine and the time of the compressing and transmitting is
less than the time to transmit the data in uncompressed form.
12. The method of claim 11, wherein the compressing achieves a
compression ratio of over 10:1.
13. The method of claim 11, wherein the time of the compressing,
transmitting, and decompressing is less than the time to broadcast
the data in uncompressed form.
14. The method of claim 11, wherein the packet includes multiple
messages.
15. The method of claim 11, wherein the compressed data is
transmitted as part of a financial data feed.
16. The method of claim 11, wherein the data field includes stock
information.
17. The method of claim 11, wherein the data field includes options
information.
18. The method of claim 11, wherein the compressing is
lossless.
19. The method of claim 11, wherein the transmitting utilizes
UDP.
20. The method of claim 11, wherein the compressing is performed on
a server and the compressed data is transmitted from the
server.
21. A method comprising: processing data of a data field of a
plurality of data fields in a packet of a financial data feed by
utilizing a list of the plurality of data fields and associated
encoders to select an encoder associated with the data field and
utilize the selected encoder on the data of the data field to
provide a processed data, wherein the size of the processed data is
less than the size of the data before the processing; and
broadcasting the processed data to a plurality of clients, wherein
the time of the processing and broadcasting is less than the time
to broadcast the data in unprocessed form.
22. The method of claim 21, wherein the processed data includes
date information.
23. The method of claim 21, wherein the processed data includes
sequence information.
24. The method of claim 21, wherein the processed data includes
multiple messages.
25. The method of claim 21, wherein the processing is performed on
a server.
26. The method of claim 21, wherein the processing is performed on
a server and the processed data is broadcast from said server.
27. The method of claim 21, wherein the processed data includes
stock information.
28. The method of claim 21, wherein the processed data includes
options information.
29. The method of claim 21, wherein the selected encoder is a
lossless encoder.
30. The method of claim 21, wherein the broadcasting utilizes UDP.
Description
TECHNICAL FIELD
The present invention relates generally to systems and method for
providing data transmission, and in particular, to systems and
method for providing accelerated transmission of data, such as
financial trading data, financial services data, financial
analytical data, company background data and news feeds,
advertisements, and all other forms or information over a
communication channel using data compression and decompression to
provide data broadcast feeds, bi-directional data transfers, and
all other forms of communication with or without security and
effectively increase the bandwidth of the communication channel
and/or reduce the latency of data transmission.
BACKGROUND
The financial markets and financial information services industry
encompass a broad range of financial information ranging from basic
stock quotations, bids, order, fulfillment, financial and
quotations to analyst reports to detailed pricing of Treasury Bills
and Callable bonds. Users of financial information can now
generally be divided into three segments--Traders, Information
Users and Analytics Users, although some users constitute
components from one or more of these categories.
Traders utilize data from financial markets such as NASDAQ, the
American Stock Exchange, the New York Stock Exchange, the Tokyo
Exchange, the London Exchange, the Chicago Options Board, and
similar institutions that offer the ability to buy and sell stocks,
options, futures, bonds, derivatives, and other financial
instruments. The need for vast quantities of information is vital
for making informed decisions and executing optimal
transactions
Thus given the importance of receiving this information over
computer networks, an improved system and method for providing
secure point-to-point solution for transparent multiplication of
bandwidth over conventional communication channels is highly
desirable.
For example, with the introduction of Nasdaq's next generation
trading system SuperMontage, Nasdaq will offer market data users an
unparalleled view into the activity, liquidity, and transparency of
the Nasdaq market.
For example, currently Nasdaq provides each market participant's
best-attributed quotation in each stock in which it makes a market.
This system known as SuperMontage allows Nasdaq to accept multiple
orders from each market participate in each stock for execution
within SuperMontage. Nasdaq offers that data, with multiple levels
of interest from individual market participants, through new data
services.
Nasdaq provides this data on both an aggregated and a detailed
basis for the top five price levels in SuperMontage. This data is
currently offered through market data vendors and broker/dealer
distributors via the following four entitlement packages:
QuoteView.sup.SM Each SuperMontage participant's best bid and
offer, as well as the best bid and offer available on SuperMontage.
DepthView.sup.SM The aggregate size, by price level, of all Nasdaq
market participants' attributed and unattributed quotations/orders
that are in the top five price levels in SuperMontage.
PowerView.sup.SM Bundled QuoteView and DepthView. TotalView.sup.SM
PowerView plus all Nasdaq market participants' attributed
quotations/orders that are in the top five price levels in
SuperMontage, in addition to the aggregate size of all unattributed
quotes/orders at each of the top five price levels.
The NASDAQ SuperMontage trading system has been cited to be
representative of trend for explosive growth in the quantity of
information for all emergent and future trading and financial
information distribution systems. Increases in processing power at
the end user sites will allow traders, analysts, and all other
interested parties to process substantially larger quantities of
data in far shorter periods of time, increasing the demand
substantially.
The ever increasing need for liquidity in the financials markets,
coupled with the competitive pressures on reducing bid/ask spreads
and instantaneous order matching/fulfillment, along the need for
synchronized low latency data dissemination makes the need for the
present invention ever more important. Depth of market information,
required to achieve many of these goals requires orders of
magnitude increases in Realtime trade information and bid/ask
pricing (Best, 2.sup.ndbest, . . . ).
A fundamental problem within the current art is the high cost of
implementing, disseminating, and operating trading systems such as
SuperMontage within the financial services industry. This is in
large part due to the high bandwidth required to transfer the large
quantities of data inherent in the operation of these systems. In
addition the processing power required to store, transmit, route,
and display the information further compounds cost and
complexity.
This fundamental problem is in large part the result of utilizing
multiple simultaneous T1 lines to transmit data. The data must be
multiplexed into separate data streams, transmitted on separate
data lines, and de-multiplexed and checked. Software solutions have
high latency and cost while hardware solutions have even higher
cost and complexity with somewhat lower latency. In addition the
synchronization and data integrity checking require substantial
cost, complexity, inherent unreliability, and latency. These and
other limitations are solved by the present invention.
Further compounding this issue is a globalization and consolidation
taking place amongst the various financial exchanges. The emergence
of localized exchanges (ECNS-Electronic Computer Networks) coupled
with the goal of 24 hour/7 day global trading will, in and of
itself, drive another exponential increase in long haul
international bandwidth requirements, while ECNs and other
localized trading networks will similarly drive domestic bandwidth
requirements. Clearly long haul links are orders of magnitude more
expensive than domestic links and the value and significance of the
present invention is at least proportionately more important.
Information users range from non-finance business professionals to
curious stock market investors and tend to seek basic financial
information and data. Analytical users on the other hand, tend to
be finance professionals who require more arcane financial
information and utilize sophisticated analytical tools to
manipulate and analyze data (e.g. for writing option
contracts).
Historically, proprietary systems, such as Thomson, Bloomberg,
Reuters and Bridge Information, have been the primary electronic
source for financial information to both the informational and
analytical users. These closed systems required dedicated
telecommunications lines and often product-specific hardware and
software. The most typical installations are land-based networking
solutions such as T1, or ISDN, and satellite-based "wireless"
solutions at speeds of 384 kbps.
Latency of financial data is critical to the execution of financial
transactions. Indeed the more timely receipt of financial data from
various sources including the New York Stock Exchange, American
Stock Exchange, National Association of Securities Dealers
(NASDAQ), Options Exchange, Commodities Exchanges, and Futures
presents a fundamental advantage to those who trade. Latency is
induced by the long time taken transmit and receive uncompressed
data or to compress and encrypt data prior to transmission, along
with the associated time to decrypt and decompress. Often current
methods of encryption and compression take as much or substantially
more time than the actual time to transmit the uncompressed,
unencrypted data. Thus another problem within the current art is
the latency induced by the act of encryption, compression,
decryption, and decompression. The present invention overcomes this
limitation within the current art.
Modern data compression algorithms suffer from poor compression,
high latency, or both. Within the present art algorithms such as
Lempel-Ziv, modified/embellished Lempel-Ziv, Binary Arithmetic, and
Huffman coding are essentially generic algorithm having a varied
effectiveness on different data types. Also small increases in
compression to the negentropy limit of the data generally require
exponentially greater periods of time and substantially higher
latency. Negentropy is herein defined as the information content
within a given piece of data. Generic algorithms are currently
utilized as data types and content format is constantly changed
within the financial industry. Many changes are gradual however
there are also abrupt changes, such as the recent switch to
decimalization to reduce granularity that has imposed substantial
requirements on data transmission bandwidth infrastructure within
the financial industry. Thus another problem within the current art
is the high latency and poor compression due to the use of generic
data compression algorithms on financial data and news feeds. This
limitation is also overcome by the present invention.
Within the financial and news feeds, data is often segregated into
packets for transmission. Further, in inquiry-response type
systems, as found in many financial research systems, the size of
request packets and also response packets is quite small. As such,
response servers often wait for long periods of time (for example
500 msec) to aggregate data packets prior to transmission back to
the inquirer. By aggregating the data, and then applying
compression, somewhat higher compression ratios are often achieved.
This then translates to lower data communications costs or more
customers served for a given amount of available communications
bandwidth. Thus another problem within the current art is the
substantial latency caused by aggregating data packets due to poor
data compression efficiency and packet overhead. This limitation is
also solved by the present invention.
Another problem within the current art is the need for data
redundancy. Currently many trading systems utilize two independent
links to compare data to verify integrity. Second, the bandwidth of
discrete last mile links, typically T1s, is limited to 1.5
Megabits/second.
Increases in bandwidth beyond this point require complex protocols
to fuse data from multiple links, adding cost and complexity, while
also increasing latency and inherent data error rates. This
limitation is also solved by the present invention.
Another limitation within the current art is that nearly all
financial institutions use one or more T1 lines to transfer
information to and from their customers. While the costs of
bandwidth have moderately decreased over recent years this trend is
slowing and the need forever increased bandwidth will substantively
overshadow any future reductions. Indeed with the recent fall-out
of the telecommunications companies the data communications price
wars will end and we could easily see an increase in the cost of
bandwidth. US Domestic T1 lines currently range from several
hundred dollars to upwards of a thousand dollars per link,
dependent upon quantity of T1 lines purchased, geographic location,
length of connection, and quality/conditioning of line. Fractional
T1 lines may also be purchased in 64 Kilobit/second increments with
some cost savings.
A standard T1 line transmits data at a rate of 1.544 megabits per
second. Accounting for framing and data transmission overhead this
means that a T1 line is capable of transmitting a 150 Kilobytes per
second. While 30.times.faster than a modem line (which provides
only 5 kilobytes per second), both are relatively slow in relation
to any reasonable level of information flow. For example,
transferring the contents of data on a single CDROM would take well
over an hour!
Thus it is likely that the capacity of many existing T1 lines will
be exceeded in the near future. For our current example let's
assume that we need to double the capacity of a T1 line. Nonnally
this is done by adding a second T1 line and combining the contents
of both with Multi-Link Point to Point Protocol (MLPP) or another
relatively complex protocol. Within the current art this is neither
necessary nor desirable. In fact any increase over the current
limitation of a T1 line results in the addition of a second line.
This limitation is overcome by the present invention.
Another limitation with the current art is the extraordinary
bandwidth required for real-time (hot) co-location processing which
has been dramatically increased as a result of the acts of terror
committed against the United States of America on Sep. 11,2001. In
order for the redundancy of any co-location to be effective, it
must be resident in a geographically disparate location; this could
be a different state, a different coast, or even a different
country. The trend towards globalization will further compound the
need for the ability to simultaneously process transactions at
geographically diverse co-locations.
It is a widely known fact within the financial industry that the
overall throughput of transactions is governed by the bandwidth and
latency of the co-location data link, along with delays associated
with synchronization, i.e. the transaction must be complete at both
locations and each location must know that the other location is
complete before the transaction is finalized.
High bandwidth links such as T3's are often utilized as part of
this backbone structure. A single T3 line has the bandwidth of
Twenty-Eight T1 lines (28.times.1.544 =43.232 megabits/second).
Thus, in the best case, a T3 line is capable of transmitting 5.4
megabytes/second. By way of comparison, the contents of a single
CDROM may be transferred in approximately two minutes with a T3
link. As stated earlier, a single T1 line would take over an hour
to transmit the same quantity of data.
The volume of real-time data that is required to operate any major
financial institution is staggering by comparison. To deal with
this issue only critical account and transaction information is
currently processed by co-locations in real-time. In fact, many
institutions use batch mode processing where the transactions are
only repeated "backed up" at the co-locations some time period
later, up to 15 minutes or longer. The limitation of highly
significant bandwidth and/or long delays with co-location
processing and long latency times is solved by the present
invention.
Thus given the importance of receiving financial information over
computer networks, an improved system and method for providing
secure point-to-point solution for transparent multiplication of
bandwidth over conventional communication channels is highly
desirable.
As previously stated, these and other limitations within the
current art are solved by the present invention.
SUMMARY OF THE INVENTION
The present invention is directed to systems and methods for
providing accelerated data transmission, and in particular to
systems and methods of providing accelerated transmission of data,
such as financial trading data, financial services data, financial
analytical data, company background data, news, advertisements, and
all other forms of information over a communications channel
utilizing data compression and decompression to provide data
transfer (secure or non-secure) and effectively increase the
bandwidth of the communication channel and/or reduce the latency of
data transmission. The present invention is universally applicable
to all forms of data communication including broadcast type systems
and bi-directional systems of any manner and any number of users or
sites.
These and other aspects, features and advantages, of the present
invention will become apparent from the following detailed
description of preferred embodiments that is to be read in
connection with the accompanying drawings.
BRIEF DESCRIPTION OF PREFERRED EMBODIMENTS
FIG. 1 is a block diagram of a system in which the present
invention may be implemented for transmitting broadcast data;
FIG. 2 is a block diagram of a system and method for providing
accelerated transmission of data over a communication channel
according to an embodiment of the present invention;
FIG. 3 is a flow diagram illustrating a method for generating
compression/decompression state machines according to one aspect of
the present invention;
FIG. 4 is a diagram illustrating an exemplary encoding table
structure according to the present invention, which may be
generated using the process of FIG. 3.
FIG. 5 is a diagram of a system/method for providing content
independent data compression, which may be implemented for
providing accelerated data transmission according to the present
invention; and
FIG. 6 is a diagram of a system/method for providing content
independent data decompression, which may be implemented for
providing accelerated data transmission according to the present
invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
The present invention is directed to systems and methods for
providing accelerated transmission of broadcast data, such as
financial data and news feeds, over a communication channel using
data compression and decompression to provide secure transmission
and transparent multiplication of communication bandwidth, as well
as reduce the latency associated with data transmission of
conventional systems.
In this disclosure, the following patents and patent applications,
all of which are commonly owned, are fully incorporated herein by
reference: U.S. Pat. No. 6,195,024, issued on Feb. 27, 2001, and
U.S. Pat. No. 6,309,424, issued on Oct. 30, 2001 and U.S. patent
application Ser. No. 10/076,013 filed on Feb. 13, 2002, Ser. No.
10/016,355, filed on Oct. 29, 2001, Ser. No. 09/481,243 filed on
Jan. 11, 2000, and Ser. No. 09/266,394 filed on Mar. 11, 1999.
In general, the term "accelerated" data transmission refers to a
process of receiving a data stream for transmission over a
communication channel, compressing the broadcast data stream in
real-time (wherein the term "real time" as used herein collectively
refers to substantially real time, or at real time, or greater than
real time) at a compression rate that increases the effective
bandwidth of the communication channel, and transmitting the
compressed broadcast data over the communication channel. The
effective increase in bandwidth and reduction of latency of the
communication channel is achieved by virtue of the fast than
real-time, real-time, near real time, compression of a received
data stream prior to transmission.
For instance, assume that the communication channel has a bandwidth
of "B" megabytes per second. If a data transmission controller is
capable of compressing (in substantially real time, real time, or
faster than real time) an input data stream with an average
compression rate of 3:1, then data can be transmitted over the
communication channel at an effective rate of up to 3*B megabytes
per second, thereby effectively increasing the bandwidth of the
communication channel by a factor of three.
Further, when the receiver is capable decompressing (in
substantially real time, real time, or faster than real time) the
compressed data stream at a rate approximately equal to the
compression rate, the point-to-point transmission rate between the
transmitter and receiver is transparently increased.
Advantageously, accelerated data transmission can mitigate the
traditional bottleneck associated with, e.g., local and network
data transmission.
If the compression and decompression are accomplished in real-time
or faster, the compressed, transmitted and decompressed data is
available before the receipt of an equivalent uncompressed stream.
The "acceleration" of data transmission over the communication
channel is achieved when the total time for compression,
transmission, and decompression, is less than the total time for
transmitting the data in uncompressed form. The fundamental
operating principle of data acceleration is governed by the
following relationship:
TABLE-US-00001 EQ [1] [T.sub.Compress + T.sub.Transmit Accelerated
+ T.sub.Decompress] < T.sub.Transmit w/o Compression Where:
T.sub.Compress = Time to Compress a Packet of Data T.sub.Transmit
Accelerated = Time to Transmit Compressed Data Packet
T.sub.Decompress = Time to Decompress the Compressed Data Packet
T.sub.Transmit w/o Compression = Time to Transmit the Uncompressed
(Original) Data Packet
As stated in Equation [1] above, if the time to compress, transmit,
and decompress a data packet is less than the time to transmit the
data in original format, then the delivery of the data is said to
be accelerated.
In the above relationship, a fundamental premise is that all
information is preferably fully preserved. As such, lossless data
compression is preferably applied. While this disclosure is
directed to transmission of data in financial networks, for
example, the concept of "acceleration" may be applied to the
storage and retrieval of data to any memory or storage device using
the compression methods disclosed in the above-incorporated U.S.
Pat. Nos. 6,195,024 and 6,309,424, and U.S. application Ser. No.
10/016,355, and the storage acceleration techniques disclosed in
the above-incorporated application. Ser. Nos. 09/481,243 and
09/266,394.
Returning to Equation [1], data acceleration depends on several
factors including the creation of compression and decompression
algorithms that are both effective (achieve good compression
ratios) and efficient (operate rapidly with a minimum of computing
processor and memory resources).
Rearranging the terms of Equation [1] we can see that the total
time to transmit data in an "accelerated" form (transmit compressed
data) is the sum of the original time to transmit the data in an
uncompressed fashion divided by the actual compression ratio
achieved, plus the time to compress and decompress the data.
T.sub.Transmit Accelerated=[T.sub.ransmit w/o
Compression/CR]+T.sub.Compress+T.sub.Decompress EQ[2] Where:
CR=Compression Ratio Thus the latency reduction is the simple
arithmetic difference between the time to transmit the original
data minus the total time to transmit the accelerated data (per
Equation 2 above), resulting in: T.sub.Latency
Reduction=T.sub.Transmit w/o Compression-T.sub.Transmit Accelerated
EQ [3] And finally the achieved "Acceleration Ratio" is defined as:
Acceleration Ratio=T.sub.Transmit w/o Compression/T.sub.Transmit
Accelerated EQ [4] A number of interesting observations come to
light from these relatively simple algebraic relationships and are
implemented within the present invention: Compression Ratio: The
present inventions achieve a consistent reduction in latency. The
data compression ratio is substantial and repeatable on each data
packet. Compression Rate: The present invention achieves a
consistent reduction in latency. Both the time to compress and
decompress the data packet must be an absolute minimum, repeatable
on each data packet, and always within predefined allowable bounds.
Packet Independence: The present invention has no packet-to-packet
data dependency. By way of example, in UDP and Multicast operations
there are no guarantees on delivery of data packets, nor on the
order of delivered data packets. IP data packets, similarly, have
no guarantee on the order of delivery also. Thus algorithms that
rely on dictionaries (Zlib, Glib, Lempel Ziv, etc.) are inherently
unreliable in any financial real-world financial data
applications.
It is to be understood that the present invention may be
implemented in various forms of hardware, software, firmware, or a
combination thereof. Preferably, the present invention is
implemented on a computer platform including hardware such as one
or more central processing units (CPU) or digital signal processors
(DSP), a random access memory (RAM), and input/output (I/O)
interface(s). The computer platform may also include an operating
system, microinstruction code, and dedicated processing hardware
utilizing combinatorial logic or finite state machines. The various
processes and functions described herein may be either part of the
hardware, microinstruction code or application programs that are
executed via the operating system, or any combination thereof
It is to be further understood that, because some of the
constituent system components described herein are preferably
implemented as software modules, the actual system connections
shown in the Figures may differ depending upon the manner in that
the systems are programmed. General purpose computers, servers,
workstations, personal digital assistants, special purpose
microprocessors, dedicated hardware, or and combination thereof may
be employed to implement the present invention. Given the teachings
herein, one of ordinary skill in the related art will be able to
contemplate these and similar implementations or configurations of
the present invention.
It should be noted that the techniques, methods, and algorithms and
teachings of the present invention are representative and the
present invention may be applied to any financial network, trading
system, data feed or other information system.
FIG. 1 is a diagram illustrating a system in which the present
invention may be implemented. The system 10 comprises content 11
and data server 12 associated with a service provider of broadcast
data. The content 11 comprises information that is processed by the
data server 12 to generate a broadcast, e.g., a news feed or
financial data feed. As explained in further detail below, the data
server 12 employs data compression to encode/encrypt the broadcast
data 11 prior to transmission over various communication channels
to one or more client site systems 20 of subscribing users, which
comprise the necessary software and hardware to decode/decrypt the
compressed broadcast data in real-time. In the exemplary embodiment
of FIG. 1, the communication channels comprise a landline 13 that
feeds the compressed broadcast data to a satellite system
comprising modem 14 and an uplink system 15, which provides a data
uplink 16 to a relay 17. The relay 17 provides data downlinks 18 to
one or more downlink systems 19.
Advantageously, the proprietary software used by the data server 12
to compress the data stream in real-time and software used by the
workstations 19 to decompress the data stream in real-time
effectively provides a seamless and transparent increase in the
transmission bandwidth of the various communication channels used,
without requiring modification of existing network
infrastructure.
Referring now to FIG. 2, a block diagram illustrates a
system/method for providing accelerated transmission of data
according to one embodiment of the present invention. More
specifically, FIG. 2 illustrates embodiments of a broadcast data
server (transmitter) and client system (receiver) for implementing
accelerated transmission and real-time processing of broadcast
data. Broadcast data 21 (comprising one or more different broadcast
types) is processed by data server 22 prior to transmission to
client 23 over a communication channel 24. The data server 22
utilizes a processor 25 (e.g., microprocessor, digital signal
processor, etc.) for executing one or more compression algorithms
26 for compressing (in real-time) the broadcast data 21 prior to
transmission. In preferred embodiments, compression is achieved
using Huffman or Arithmetic encoding, wherein one or more state
machines 27-27n are constructed based on a-priori knowledge of the
structure and content of one or more given broadcast and data
feeds.
As explained in further detail below, each state machine 27-27n
comprises a set of compression tables that comprise information for
encoding the next character (text, integer, etc.) or sequence of
characters in the broadcast data feed, as well as pointers which
point to the next state (encoding table) based on the character or
character sequence. As explained in greater detail below, a
skeleton for each state machine 27-27n (nodes and pointers) is
preferably built by finding sequences of characters (n-tuples) that
frequently appear in a given data input. Once a skeleton has been
determined, a large set of data is processed through the system and
counts are kept of character n-tuples for each state. These counts
are then used to construct the compression tables associated with
the state machine to provide statistical compression. The
compressed data is transmitted over the communication channel 24
via a communication stack using any suitable protocol (e.g., RTP
(real time protocol) using RTCP (real-time control protocol),
TCP/IP, UDP, or any real-time streaming protocol with suitable
control mechanism).
Similarly, the client 23 comprises a processor 30 for executing one
or more decompression algorithms 31. Depending one the data feed
type, one of a plurality of decompression state machines 32-32n are
used to decompress the compressed data stream received by the
client 23 via communication stack 34. Each state machine 32-32n
comprises a set of decompression tables 33-33n that comprise
information for decode the next encoded character (or symbol) or
sequence of symbols in the compressed broadcast data feed, as well
as pointers which point to the next state based on the symbol or
symbol sequence. For each compression state machine 27-27n in the
data server, a corresponding decompression state machine 32-32n is
needed in the client 23 to decompress the associated data
stream.
Advantageously, a compression/decompression scheme according to the
present invention using Huffman or Arithmetic encoding provides
secure transmission via de facto or virtual "encryption" in a
real-time environment. Indeed, virtual encryption is achieved by
virtue of the fast, yet complex, data compression using Huffman
tree, for example, without necessarily requiring actual encryption
of the compressed data and decryption of the compressed data.
Because of the time-sensitive nature of the market data, and the
ever-changing and data-dependent nature of the arithmetic scheme,
decryption is virtually impractical, or so complex and useless as
to render the data worthless upon eventual decoding.
However, data compression using Huffman or Arithmetic encoding
yields encoded data that is very difficult to decode than current
encryption schemes such as plain text or simple bit shuffling codes
as currently used by broadcast service providers. An attacker must
have the compression model and the tables used to compress the data
stream to be able to obtain useful information from it. Thus, at
one level of security, the client-side decompression tables are
preferably stored in encrypted form and are decrypted on being
loaded into the processor 30 (e.g., general purpose processor, DSP,
etc.) using an encryption/decryption key that is validated for a
subscribing user. In this manner, a client will be unable to use
the tables on other processors or sites or after terminating a
service contract.
Since Huffman compression uses the same bit code for a character
each time it appears in a given context, an attacker with a very
large data set of compressed and uncompressed data could possibly
reconstruct the tables, assuming the overall model were known.
Arithmetic compression, on the other hand, generates different bit
patterns for the same character in the same context depending on
surrounding characters. Arithmetic encoding provides at least an
order of magnitude more difficult to recover the tables from the
compressed and uncompressed data streams.
The following is a detailed discussion of a compression scheme
using Huffman or Arithmetic encoding for providing accelerated
transmission of broadcast data according to one aspect of the
present invention. It is to be appreciated that the present
invention is applicable with any data stream whose statistical
regularity may be captured and represented in a state machine
model. For example, the present invention applies to packetized
data streams, in which the packets are limited in type format and
content.
In one embodiment using Huffman or Arithmetic encoding, each
character or character sequence is encoded (converted to a binary
code) based on the frequency of character or character sequence in
a given "context". For a given context, frequently appearing
characters are encoded with few bits while infrequently appearing
characters are encoded with more bits. High compression ratios are
obtained if the frequency distribution of characters in most
contexts is highly skewed with few frequently appearing characters
and many characters seldomly (or never) appear.
Referring now to FIG. 3, a flow diagram illustrates a method for
generating compression/decompression state machines according to
one aspect of the present invention. The "context" in which a
character (or character sequence) is encoded in a given broadcast
stream is based on a "global state" that represents packet type and
large-scale structure and the previous few characters. The first
step in building a compression scheme involves selecting a global
state system based on the packet structure of the broadcast model
(step 40). More specifically, a global state system is constructed
based on a priori knowledge of the data stream model, e.g., the
packet type frequency and structure of the broadcast model. By way
of example, one model for financial data may comprise four global
states representing: a beginning of packet, an options packet, a
NYSE (New York Stock Exchange) packet and some other packet type.
Further, additional codes may be added to the encoding tables to
indicate global state transitions (e.g., for an end of packet code
in the broadcast model). If there is internal structure to packets,
such as a header with different statistics than the body,
additional global states could be added.
Once a global state system is selected, training samples from an
associated data stream are passed through the global model to
acquire counts of frequencies of the occurrence of n-tuple
character sequences ending in each of the model states (step 41).
In a preferred embodiment, the n-tuples comprise character
sequences having 1, 2 and 3 characters. Using the acquired counts,
sub-states (or "local states") of the predefined global states are
constructed based on previous characters in the data stream. A
local state may depend on either none, 1, 2, or 3 (or more)
previous characters in the stream. To provide a practical
limitation, a predetermined count threshold is preferably applied
to the count data (step 42) and only those sequences that occur
more often than the count threshold are added as local states (step
43). For example, if a three-character sequence does not occur
sufficiently frequently, the count for the last two characters is
tested, etc.
It is to be understood that any character sequence length "n" may
be implemented depending on the application. The longer the allowed
character sequence, the more memory is needed to store the encoding
tables and/or the lower the count threshold should be set.
As samples of the data are passed through the state model,
character (and transition code) counts for each context are
accumulated. These counts are used to build the Huffman or
Arithmetic coding tables. The construction of the global and local
models is an iterative process. The count threshold for forming
local states can be adjusted depending on the application. For
instance, a larger threshold will result in less local states but
less compression as well. Further, a comparison of statistics in
local or global states may suggest adding or deleting global
states.
The construction of the global model requires knowledge of the data
stream packet structure. The construction of the local states is
automatic (once the threshold is set).
FIG. 4 is a diagram of an exemplary state diagram (or encoding
table structure) according to the present invention, which may be
generated using the process of FIG. 3.
As noted above with reference to FIGS. 1 and 2, a compression
scheme according to the present invention may be implemented in any
system to provide accelerated data transmission to multiple client
site systems. Preferably, the client site systems may connect at
any time, so minimal immediate history may be used (since a newly
connected site must be able to pick up quickly). A system according
to an embodiment of the present invention uses statistical
compression (Huffman or Arithmetic coding) using fixed (or
adaptive) tables based on the statistics of a data feed sample. As
noted above, it has been determined that the statistical
compression schemes described herein are well adapted for use with
structured data streams having repetitive data content (e.g., stock
symbols and quotes, etc.) to provide fast and efficient data
compression/decompression.
The following discussion provides further details regarding the
preparation of statistical-based encoding tables and their use for
compression/decompression according to the present invention.
During a data compression process, the selection of which encoding
table to use for compression is preferably based on up to n (where
n is preferably equal to 3) preceding characters of the message. In
an exemplary broadcast model tested by the present inventors, a
data stream comprises messages that begin with an ID code in the
range 0-31 with the remainder of the message being characters in
the range 32-127. It was found that approximately half of the
messages in a given sample began with ID code 0x0c and half of the
remainder began with ID code 0x0f. Thus, a separate encoding table
is preferably used for a message ID code. Further, separate table
sets are used for messages beginning with 0x0c and with 0x0f, with
the remaining messages lumped together in another table.
Each table has an additional termination code. The termination code
in a "start table" indicates the end of a compression block. The
termination code in all other tables indicates the end of the
message. Thus, the start table comprises 33 entries and all other
tables have 97 entries.
Using one table for each 3-character context would require
prohibitive amounts of memory. For example, a complete
one-character context would require 33+3*97=324 tables. Then, a
complete two-character context would require 324*97=31,428 tables.
And finally, a complete three-character context would require
324*97*97=3,048,516 tables. Preferably, as described above, the
application of a count threshold at each context size reduces the
amount of tables. Only when a context occurs at greater than the
threshold rate in the sample will a table be created for that
context.
Each table entry includes a link to the next table to be used. For
instance, in an "abc" context table, the entry for next character
"d" would point to the "bed" table, if such table was created. If
such table was not created, the entry for next character "d" would
point to the "cd" table, if such table existed. If no "cd" table
exists, the "d" table would be used and if that fails, a base table
for the message type would be used.
For a client site system to pick up the broadcast feed at any time,
clearly identifiable synchronization points are preferably included
in the compressed data stream. In a preferred embodiment, data is
compressed in blocks with each block comprising some number of
complete messages. Preferably, each compressed block ends with at
least four bytes with each bit being logic 1 and no interior point
in the compressed block will comprise 32 consecutive 1 bits. The
compressed block preferably begins with two bytes giving the
decompressed size of the block shifted to guarantee that the first
byte of the compressed block is not all 1's. Thus, to achieve
synchronization, the client site system can scan the input
compressed data stream for 4 bytes of 0xff, wherein the next byte
not equal to 0xff is deemed the start of a compressed block. In
other words, the receiver will accumulate the compressed data until
at least a sequence of 4 bytes each having a value of 0xff is
detected in the input stream, at which point decompression will
commence on the compressed input stream.
In another embodiment of the present invention, if a compressed
block is more than 6 bytes longer than the uncompressed data, the
data block is transmitted uncompressed preceded by the shifted
two-byte count with the high bit set and trailed by 4 bytes of
0xff.
The following is discussion of a method for preparing Huffman
Tables according to one aspect of the present invention. The
Huffman codes generated by a conventional optimal algorithm have
been modified in various ways in accordance with the present
invention. First, in order that there not be 32 consecutive one
bits in the data stream except at the end of a compression block, a
termination code in each table comprises all 1 bits.
Further, to reduce space required for decompression tables, and
ensure no sequence of 32 1 bits, each code is preferably decoded as
follows:
a) The first 7 bits are used to index into a table. If the
character code is no more than 7 bits, it can be read directly;
b) otherwise, some number N of initial bits is discarded and the
next 7 bits are used to index a second table to find the
character.
Based on these steps, preferably, no character code can use more
than 14 bits and all codes of more than 7 bits must fit into the
code space of the N initial bits. If N is 3, for instance, then no
code can use more than 10 bits.
To achieve this, the code space required for all optimal codes of
more than 7 bits is first determined, following by a determining
the initial offset N. Every code comprising more than N+7 bits is
preferably shortened, and other codes are lengthened to balance the
code tree. It is possible that this may cause the code space for
codes over 7 bits to increase so that N may need to be decreased.
Preferably, this process is performed in a manner that causes
minimal reduction in the efficiency of the codes.
The above modifications to convention optimal algorithm yields
codes in which no non-termination code ends in more than 7 1 bits,
no non-termination code begins with more than 6 1 bits, no
termination code is more than 14 1 bits and no non-termination
packet start code begins with more than 5 1 bits. Thus, in the
middle of a packet, a sequence of no more than 13 bits of logic 1
can occur, while, at the end of a packet, a sequence of no more
than 26 bits of logic 1 can occur.
In another embodiment of the present invention, Arithmetic
compression can be used instead of Huffman encoding. The tables for
Arithmetic encoding are preferably constructed such that a sequence
of 32 bits of logic 1 will not occur in the interior of a message
(which is important for a random sign-on in the middle of the
stream).
Arithmetic compression provides an advantage of about 6% better
compression than Huffman and uses half as much memory for tables,
which allows the number of tables to be increased). Indeed, the
addition of more tables and/or another level of tables yields more
efficient compression. Although Arithmetic compression may take
about 6 times as long as Huffman, this can certainly be improved by
flattening the subroutine call tree (wherein there is a subroutine
call for each output bit.)
In summary, a compression scheme according to one aspect of the
invention utilizes a state machine, wherein in each state, there is
a compression/decompression table comprising information on how to
encode/decode the next character, as well as pointers that
indicated which state to go to based on that character. A skeleton
of the state machine (nodes and pointers) is preferably built by
finding sequences of characters that appear often in the input.
Once the skeleton has been determined, a large set of data is run
through the system and counts are kept of characters seen in each
state. These counts are then used to construct the encode/decode
tables for the statistical compression.
Other approaches may be used to build the skeleton of the state
machine. A very large fraction of the traffic on a certain feed
consists of messages in the digital data feed format, which is
fairly constrained. It may be possible to build by hand a skeleton
that takes into account this format. For instance, capital letters
only appear in the symbol name at the beginning. This long-range
context information can be represented with our current approach.
Once a basic skeleton is in place, the structure could be extended
for sequences that occur frequently.
The above-described statistical compression schemes provide
content-dependent compression and decompression. In other words,
for a given data stream, the above schemes are preferably
structured based on the data model associated with the given
stream. It is to be appreciated, however, that other compression
schemes may be employed for providing accelerated data transmission
in accordance with the present invention for providing effectively
increased communication bandwidth and/or reduction in latency. For
instance, in another embodiment of the present invention, the data
compression/decompression techniques disclosed in the
above-incorporated U.S. Pat. No. 6,195,024, entitled "Content
Independent Data Compression Method and System" may be used in
addition to, or in lieu of, the statistical based compression
schemes described above.
In general, a content-independent data compression system is a data
compression system that provides an optimal compression ratio for
an encoded stream regardless of the data content of the input data
stream. A content-independent data compression method generally
comprises the steps of compressing an input data stream, which
comprises a plurality of disparate data types, using a plurality of
different encoders. In other words, each encoder compresses the
input data stream and outputs blocks of compressed data. An encoded
data stream is then generated by selectively combining compressed
data blocks output from the encoders based on compression ratios
obtained by the encoders. Because a multitude of different data
types may be present within a given input data stream, or data
block, to it is often difficult and/or impractical to predict the
level of compression that will be achieved by any one encoding
technique. Indeed, rather than having to first identify the
different data types (e.g., ASCII, image data, multimedia data,
signed and unsigned integers, pointers, etc.) comprising an input
data stream and selecting a data encoding technique that yields the
highest compression ratio for each of the identified data types,
content-independent data compression advantageously applies the
input data stream to each of a plurality of different encoders to,
in effect, generate a plurality of encoded data streams. The
plurality of encoders are preferably selected based on their
ability to effectively encode different types of input data.
Ultimately, the final compressed data stream is generated by
selectively combining blocks of the compressed streams output from
the plurality of encoders. Thus, the resulting compressed output
stream will achieve the greatest possible compression, regardless
of the data content.
In accordance with another embodiment of the present invention, a
compression system may employ both a content-dependent scheme and a
content-independent scheme, such as disclosed in the
above-incorporated application. Ser. No. 10/016,355. In this
embodiment, the content-dependent scheme is used as the primary
compression/decompression system and the content-independent scheme
is used in place of, or in conjunction with, the content dependent
scheme, when periodically checked "compression factor" meets a
predetermined threshold. For instance, the compression factor may
comprise a compression ratio, wherein the compression scheme will
be modified when the compression ratio falls below a certain
threshold. Further, the "compression factor" may comprise the
latency of data transmission, wherein the data compression scheme
with be modified when the latency of data transmission exceeds a
predetermined threshold.
Indeed, as explained above, the efficiency of the content-dependent
compression/decompression schemes described herein is achieved,
e.g., by virtue of the fact that the encoding tables are based on,
and specifically designed for, the known data model. However, in
situations where the data model is may be modified, the efficiency
of the content-dependent scheme may be adversely affected, thereby
possibly resulting in a reduction in compression efficiency and/or
an increase in the overall latency of data transmission. In such a
situation, as a backup system, the data compression controller can
switch to a content-independent scheme that provides improved
compression efficiency and reduction in latency as compared to the
primary content-dependent scheme.
In yet another embodiment of the present invention, when the
efficiency of a content-dependent scheme falls below a
predetermined threshold based on, e.g., a change in the data
structure of the data stream, the present invention preferably
comprises an automatic mechanism to adaptively modify the encoding
tables to generate optimal encoding tables (using the process
described above with reference to FIG. 3).
FIG. 5 is a detailed block diagram illustrates an exemplary
content-independent data compression system 110 that may be
employed herein. Details of this data compression system are
provided in U.S. Pat. No. 6,195,024, which is fully incorporated
herein by reference. In this embodiment, the data compression
system 110 accepts data blocks from an input data stream and stores
the input data block in an input buffer or cache 115. It is to be
understood that the system processes the input data stream in data
blocks that may range in size from individual bits through complete
files or collections of multiple files. Additionally, the input
data block size may be fixed or variable. A counter 120 counts or
otherwise enumerates the size of input data block in any convenient
units including bits, bytes, words, and double words. It should be
noted that the input buffer 115 and counter 120 are not required
elements of the present invention. The input data buffer 115 may be
provided for buffering the input data stream in order to output an
uncompressed data stream in the event that, as discussed in further
detail below, every encoder fails to achieve a level of compression
that exceeds an a priori specified minimum compression ratio
threshold.
Data compression is performed by an encoder module 125 that may
comprise a set of encoders E1, E2, E3 . . . En. The encoder set E1,
E2, E3 . . . En may include any number "n" (where n may=1) of those
lossless encoding techniques currently well known within the art
such as run length, Huffman, Lempel-Ziv Dictionary Compression,
arithmetic coding, data compaction, and data null suppression. It
is to be understood that the encoding techniques are selected based
upon their ability to effectively encode different types of input
data. It is to be appreciated that a full complement of encoders
are preferably selected to provide a broad coverage of existing and
future data types.
The encoder module 125 successively receives as input each of the
buffered input data blocks (or unbuffered input data blocks from
the counter module 120). Data compression is performed by the
encoder module 125 wherein each of the encoders E1 . . . En
processes a given input data block and outputs a corresponding set
of encoded data blocks. It is to be appreciated that the system
affords a user the option to enable/disable any one or more of the
encoders E1 . . . En prior to operation. As is understood by those
skilled in the art, such feature allows the user to tailor the
operation of the data compression system for specific applications.
It is to be further appreciated that the encoding process may be
performed either in parallel or sequentially. In particular, the
encoders E1 through En of encoder module 125 may operate in
parallel (i.e., simultaneously processing a given input data block
by utilizing task multiplexing on a single central processor, via
dedicated hardware, by executing on a plurality of processor or
dedicated hardware systems, or any combination thereof). In
addition, encoders E1 through En may operate sequentially on a
given unbuffered or buffered input data block. This process is
intended to eliminate the complexity and additional processing
overhead associated with multiplexing concurrent encoding
techniques on a single central processor and/or dedicated hardware,
set of central processors and/or dedicated hardware, or any
achievable combination. It is to be further appreciated that
encoders of the identical type may be applied in parallel to
enhance encoding speed. For instance, encoder E1 may comprise two
parallel Huffman encoders for parallel processing of an input data
block.
A buffer/counter module 130 is operatively connected to the encoder
module 125 for buffering and counting the size of each of the
encoded data blocks output from encoder module 125. Specifically,
the buffer/counter 130 comprises a plurality of buffer/counters
BC1, BC2, BC3 . . . BCn, each operatively associated with a
corresponding one of the encoders E1 . . . En. A compression ratio
module 135, operatively connected to the output buffer/counter 130,
determines the compression ratio obtained for each of the enabled
encoders E1 . . . En by taking the ratio of the size of the input
data block to the size of the output data block stored in the
corresponding buffer/counters BC1 . . . BCn. In addition, the
compression ratio module 135 compares each compression ratio with
an a priori-specified compression ratio threshold limit to
determine if at least one of the encoded data blocks output from
the enabled encoders E1 . . . En achieves a compression that
exceeds an a priori-specified threshold. As is understood by those
skilled in the art, the threshold limit may be specified as any
value inclusive of data expansion, no data compression or
expansion, or any arbitrarily desired compression limit. A
description module 138, operatively coupled to the compression
ratio module 135, appends a corresponding compression type
descriptor to each encoded data block which is selected for output
so as to indicate the type of compression format of the encoded
data block. A data compression type descriptor is defined as any
recognizable data token or descriptor that indicates which data
encoding technique has been applied to the data. It is to be
understood that, since encoders of the identical type may be
applied in parallel to enhance encoding speed (as discussed above),
the data compression type descriptor identifies the corresponding
encoding technique applied to the encoded data block, not
necessarily the specific encoder. The encoded data block having the
greatest compression ratio along with its corresponding data
compression type descriptor is then output for subsequent data
processing or transmittal. If there are no encoded data blocks
having a compression ratio that exceeds the compression ratio
threshold limit, then the original unencoded input data block is
selected for output and a null data compression type descriptor is
appended thereto. A null data compression type descriptor is
defined as any recognizable data token or descriptor that indicates
no data encoding has been applied to the input data block.
Accordingly, the unencoded input data block with its corresponding
null data compression type descriptor is then output for subsequent
data processing or transmittal.
Again, it is to be understood that the embodiment of the data
compression engine of FIG. 5 is exemplary of a preferred
compression system which may be implemented in the present
invention, and that other compression systems and methods known to
those skilled in the art may be employed for providing accelerated
data transmission in accordance with the teachings herein. Indeed,
in another embodiment of the compression system disclosed in the
above-incorporated U.S. Pat. No. 6,195,024, a timer is included to
measure the time elapsed during the encoding process against an a
priori-specified time limit. When the time limit expires, only the
data output from those encoders (in the encoder module 125) that
have completed the present encoding cycle are compared to determine
the encoded data with the highest compression ratio. The time limit
ensures that the real-time or pseudo real-time nature of the data
encoding is preserved. In addition, the results from each encoder
in the encoder module 125 may be buffered to allow additional
encoders to be sequentially applied to the output of the previous
encoder, yielding a more optimal lossless data compression ratio.
Such techniques are discussed in greater detail in the
above-incorporated U.S. Pat. No. 6,195,024.
Referring now to FIG. 6, a detailed block diagram illustrates an
exemplary decompression system that may be employed herein or
accelerated data transmission as disclosed in the
above-incorporated U.S. Pat. No. 6,195,024. In this embodiment, the
data compression engine 180 accepts compressed data blocks received
over a communication channel. The decompression system processes
the input data stream in data blocks that may range in size from
individual bits through complete files or collections of multiple
files. Additionally, the input data block size may be fixed or
variable.
The data decompression engine 180 comprises an input buffer 155
that receives as input an uncompressed or compressed data stream
comprising one or more data blocks. The data blocks may range in
size from individual bits through complete files or collections of
multiple files. Additionally, the data block size may be fixed or
variable. The input data buffer 55 is preferably included (not
required) to provide storage of input data for various hardware
implementations. A descriptor extraction module 160 receives the
buffered (or unbuffered) input data block and then parses,
lexically, syntactically, or otherwise analyzes the input data
block using methods known by those skilled in the art to extract
the data compression type descriptor associated with the data
block. The data compression type descriptor may possess values
corresponding to null (no encoding applied), a single applied
encoding technique, or multiple encoding techniques applied in a
specific or random order (in accordance with the data compression
system embodiments and methods discussed above).
A decoder module 165 includes one or more decoders D1 . . . Dn for
decoding the input data block using a decoder, set of decoders, or
a sequential set of decoders corresponding to the extracted
compression type descriptor. The decoders D1 . . . Dn may include
those lossless encoding techniques currently well known within the
art, including: run length, Huffman, Lempel-Ziv Dictionary
Compression, arithmetic coding, data compaction, and data null
suppression. Decoding techniques are selected based upon their
ability to effectively decode the various different types of
encoded input data generated by the data compression systems
described above or originating from any other desired source.
As with the data compression systems discussed in the
above-incorporated U.S. Pat. No. 6,195,024, the decoder module 165
may include multiple decoders of the same type applied in parallel
so as to reduce the data decoding time. An output data buffer or
cache 170 may be included for buffering the decoded data block
output from the decoder module 165. The output buffer 70 then
provides data to the output data stream. It is to be appreciated by
those skilled in the art that the data compression system 180 may
also include an input data counter and output data counter
operatively coupled to the input and output, respectively, of the
decoder module 165. In this manner, the compressed and
corresponding decompressed data block may be counted to ensure that
sufficient decompression is obtained for the input data block.
Again, it is to be understood that the embodiment of the data
decompression system 180 of FIG. 6 is exemplary of a preferred
decompression system and method which may be implemented in the
present invention, and that other data decompression systems and
methods known to those skilled in the art may be employed for
providing accelerated data transmission in accordance with the
teachings herein.
It is to be appreciated that a data transmission acceleration
system according to the present invention offers a business model
by which market data vendors and users in the financial information
services industry can receive various benefits. For example, the
present invention affords transparent multiplication of bandwidth
with minimal latency. Experiments have shown that increased
bandwidth of up to 3 times can be achieved with minimal latency.
Furthermore, proprietary hardware, including chip and board
designs, as well as custom embedded and application software and
algorithms associated with accelerated data transmission provide a
cost-effective solution that can be seamlessly integrated with
existing products and infrastructure. Moreover, the data
acceleration through "real-time" compression and decompression
affords a dramatic reduction in ongoing bandwidth costs. Further,
the present invention provides mechanism to differentiate data
feeds from other vendors via enriched content or quantity of the
data feed.
In addition, a data compression scheme according to the present
invention provides dramatically more secure and encrypted feed from
current levels, thus, providing the ability to employ a secure and
accelerated virtual private network over the Internet for
authorized subscribers or clients with proprietary hardware and
software installed.
Moreover, the present invention offers the ability to reduce a
client's ongoing monthly bandwidth costs as an incentive to
subscribe to a vendor's data feed service.
The present invention is readily extendable for use on a global
computer network such as the Internet. This is significant since it
creates a virtual private network and is important for the market
data vendors and others due to its reduced cost in closed
network/bandwidth solutions. In effect, the data vendors get to
"ride for free" over the world's infrastructure, while still
providing the same (and enhanced) services to their customers.
In yet another embodiment of the present invention a highly
optimized data compression and decompression system is utilized to
accelerate data transfers for data transmission feeds. This type of
compression achieves very high compression ratios (over 10:1) on
financial data feeds such as Nasdaq Quote Dissemination Service
Data (NQDS) and SuperMontage Services. The information utilized to
develop the methods described herein for Nasdaq has been garnered
solely from public knowledge through specifications available from
the Nasdaq Trader and Nasdaq websites. The techniques disclosed
herein are broadly applicable to all financial data feeds and
information or trading services.
Three types of encoding are utilized dependent upon the data fields
and packet structure. In the event that a data field is
unrecognizable then content independent data compression is
preferably used, as previously discussed herein.
Variable Length Encoding
The basic unit of the compression process is the code. Each message
field or set of set of fields being compressed together is assigned
one or more codes in the range 0 . . . N. The code for a single
character field is the ASCII value of the field minus 32 since all
characters are in the range 32 to 127.
For various reasons, additional (escape) codes may be added to
those for field values. For example, the category field has an
escape code to indicate the end of a block and another to allow
encoding of messages, which do not match the current format.
A basic technique used is variable rate encoding of symbols. In
this approach, different amounts of the output bits are used to
transmit the codes within a set. Higher frequency codes use less
output bits while lower frequency codes use more output bits. Thus
the average number of bits is reduced. Two methods of accomplishing
this are used. The faster method uses a variant of Huffman coding
while the slower method uses a form of Arithmetic coding.
In Huffman coding, each code is represent by an integral number of
bits. The code sizes are computed using the standard algorithm and
then (possibly) adjusted to facilitate table driven decoding (for
instance, limiting codes to at most 16 bits). In the table driven
decoding method used, there is a 256 clement base table and two 256
element forwarding table. At each step, the next 8 bits of the
input are used to index into the base table. If the code is
represented in no more than 8 bits, it will be found directly.
Otherwise, there will be a forwarding entry indicating which
forwarding table to use and how many input bits to discard before
using the next 8 bits as an index. The entry determining the result
also indicates how many bits of the input to discard before
processing the next field.
In arithmetic coding, the message is essentially represented as the
(approximate) product of fractions with base 16384. The numerators
of the fractions are proportional to the frequencies with which the
codes appear in the training data. The number of output bits used
to represent a code is the base 2 logarithm of the fraction. Thus
codes which appear in almost all messages may be represented with
fractions of a bit.
Single Character Codes
For arithmetic coding, all single character fields arc encoded as
the ASCII value-32+the number of escape codes. For Huffman coding,
certain single character message fields are encoded in the same
way. These include:
MM Trade Desk
Quote Condition
Inside Indicator
Quote Type
Other single character fields, which have a single value that
occurs most of the time, are encoded as multiple character fields
(see next). In Huffman coding the smallest representation for a
code is 1 bit. By combining these fields, we may encode the most
common combination of values in I bit for the whole set. These
include:
Message Category+Message Type
Session Identifier+Originator ID
PMM+Bid Pricc Denominator+Ask Price Denominator (Quotes)
Inside Status+Inside Type
Inside Bid Denominator+Inside Bid MC
Inside Ask Denominator+Inside Ask MC
UPC Indicator+Short Sale Bid Tick
Market of Origin+Reason
Small Set Multiple Character Codes
Multiple character fields with a small number of common values and
certain combinations of single character fields are encoded based
on the frequency of the combinations. A list of common combinations
is used together with an escape code.
The common combinations are encoded using the corresponding code.
All other combinations are encoded by the escape code followed by
the (7 bit) ASCII values for the characters in the combination. The
fields include the field sets above for Huffman coding as well as
the following for both approaches:
Retransmission Requester
MM Location
Currency Code
Large Set Multiple Character Codes
Multiple character alphabetic or alphanumeric fields for which a
large number of values are possible (Issue Symbol and MMID/MPID)
are encoded as follows. Trailing spaces for Issuc Symbols arc
deleted. Then the result is encoded using:
Variable length codes for a list of the most common values together
with escapes for the possible lengths of values not in the
list.
A table for the first character of the field.
A table for subsequent characters in the field.
If a value is in the list of most common values, it is encoded with
the corresponding code. Otherwise, the value is encoded by sending
the escape code corresponding to the (truncated) length of the
value, followed by the code for the first character, which is then
followed by codes for the remaining characters.
Absolute Numeric Values
Numeric fields arc transmitted by sending a variable length code
for the number of significant bits of the value followed by the
bits of the value other than the most significant bit (which is
implicitly 1). For example, 27 (a 5 bit value) would be represented
by the code for a 5 bit value followed by the 4 least significant
bits (11).
These fields include:
Short Bid Price
Long Bid Price
Short Bid Size
Long Bid Size
Short Ask Size
Long Ask Size
Short Inside Bid Size
Long Inside Bid Size
Short Inside Ask Size
Long Inside Ask Size
Relative Numeric Values
Numeric fields expected to be close to the value of numeric values
occurring earlier in the message are encoded by encoding the
difference between the new value and the base value as follows:
If the difference in non-negative and less than 1/8 of the base
value, the difference is encoded by sending a variable length code
for the number of significant bits of the difference followed by
the bits of the difference other than the most significant bit
(which is implicitly 1). Otherwise, the new value is encoded by
sending a variable length code for the number of significant bits
of the value followed by the bits of the value other than the most
significant bit (which is implicitly 1). The difference significant
bit codes and the value significant bit codes are mutually
exclusive. The following fields are encoded using the difference
compared to the field in parentheses:
Short Ask Price (Bid Price)
Long Ask Price (Bid Price)
Short Inside Bid Price (Bid Price)
Short Inside Ask Price (Inside Bid Price)
Long Inside Bid Price (Bid Price)
Long Inside Ask Price (Inside Bid Price)
Differences
Both time and Message Sequence Number are encoded as the difference
between the new value and a previous value within the compression
block. This is transmitted using a code giving the sign of the
difference and the number of significant bits in the absolute value
of the difference followed by the bits of the absolute value other
than the first.
Date
Each message within a compression block is expected to have the
same date. The base date is transmitted at the beginning of the
block as 7 bits of year, 4 bits of month and 5 bits of day of the
month. If the date of a message is different than that of the
block, a special escape code is used in place of the encoding of
the sequence number and time. This is followed by the year, month
and day as above followed by the time in seconds (17 bits) and the
sequence number (24 bits).
Message Sequence Number and Time
Message time is converted to seconds after midnight. For all
retransmitted messages (Retransmission Requester not "O"), the time
is transmitted as a 17-bit value followed by the Message Sequence
Number transmitted as a 24-bit value. If the date is not the same
as the block date, a time value of 0x1ffff is used as an escape
code.
For the first original transmission message in a block, the Message
Sequence Number and time are transmitted in the same way.
For arithmetic coding of all other original transmission messages
in a block, the Message Sequence Number is transmitted as the
encoded change from the Massage Sequence Number of the preceding
original transmission message. Similarly, the time of all other
original transmission messages is encoded as the difference from
the previous original transmission message. An escape code in the
Message Sequence Number Difference Table is used to indicate that
the date is not the same as the block date.
Since almost all sequence number changes are 1 and almost all time
changes are 0, we can save a bit (while Huffman coding) by encoding
time and sequence number together.
This is done as follows: The most common values for both time and
sequence number changes are 0 and 1 so there are three
possibilities for each: 0, 1 and something else. Together this
yields nine possibilities. An escape code is added to indicate a
date different from the block date. To transmit the sequence number
and time, the code corresponding the correct combination is first
sent and then, if the time difference is not 0 or 1, the difference
code for time followed by the difference code for sequence number
(if required) is sent.
Unexpected Message Types
For administrative messages or non-control messages of unexpected
category or type, the body of the message (the part after the
header) is encoded as a 10-bit length field followed by the
characters of the body encoded as 7-bit ASCII. Any Quotation
message with an unexpected Inside Indicator value will have the
remainder of the message encoded similarly.
Termination Code and Error Detection
Each compression block is terminated by an escape code of the
message header category or category-type table. If this code is not
found before the end of the block or if it is found too soon in the
block, an error is returned. It is highly unlikely that a
transmission error in the compressed packet could result in
decoding so as to end at the same place as the original. The
exception to this would be errors in transmitting bits values such
as date, time or sequence number or the least significant bits of
encoded values or changes. For additional error detection, a CRC
check for the original could be added to compressed block.
Experimental Results
The aforecited Data Acceleration Methods were successfully applied
to data captured on NASDAQ's NQDS feed. The data captured was first
analyzed to optimize the Data Acceleration Methods. Essentially two
distinct data rates were evaluated; one similar to the upcoming
NASDAQ SuperMontage rate of 9.0 Megabits/sec and the second being
the maximum data rate of the NQDS feed of 221 Kilobits/sec. In
addition, two modes of data acceleration were applied--one
utilizing Arithmetic and the other utilizing Huffman
techniques.
The Arithmetic routines typically use 40% more CPU time than the
Huffman routines and achieve approximately 15% better compression.
On average the compression ratio for the SuperMontage data rate
(9.0 Megabits/sec) utilizing Arithmetic Mode, yielded a value of
9.528 with a latency under 10.0 ms. This effectively says that the
NQDS feed operating at a SuperMontage rate could be transmitted
over one T1 line! Further overall latency can be reduced from 500
msec to something approaching 10 milliseconds if routing delays are
reduced. Since the amount of data is substantially less, it will be
easier and much more cost efficient to reduce routing delays.
Further, since the quantity of transmitted bits is substantially
smaller, the skew amongst transmitted packets will also be
proportionately lower.
The average compression ratio for the standard NQDS data rate (221
Kbits/sec) was 9.3925 for the Arithmetic Mode with a latency under
128 ms. The higher latency is due to the time required to
accumulated data for blocking. Since the present invention allows
for very high compression ratios with small blocks of data, the
latency can be reduced substantially from 128 msec without a loss
in compression ratio. This effectively says that the existing NQDS
feed could be transmitted over one-half of a 56 Kilobit/sec modem
line. Other advantages of using data acceleration according to the
invention is that such methods inherently provide (i) a high level
of encryption associated with the Arithmetic Mode (with no
subsequent impact on latency) and (ii) error detection capability
of the decompression methods at the end user site. The first
benefit produces additional levels of security for the transmitted
data and the second benefit guarantees that corrupted data will not
be displayed at the end user site. Furthermore, the need to
dynamically compare the redundant data feeds at the end user site
is eliminated.
In yet another embodiment of the present invention the aforecited
algorithms and all other data compression/decompression algorithms
may be utilized in a data field specific compiler that is utilized
to create new data feed and data stream specific compression
algorithms.
A data field description language is utilized to define a list of
possible data fields and parameters along with associated data
compression encoders and parameter lists. In one embodiment of the
invention the data fields are defined utilizing the following
convention:
TABLE-US-00002 <start list> <list file name (optional)>
<data field a descriptor, optional parameters> [data field a
compression algorithm x, optional parameters] <data field b
descriptor, optional parameters> [data field b compression
algorithm y, optional parameters] . . . <data field m
descriptor, optional parameters> [data field m compression
algorithm n, optional parameters] <end list>
Thus start list and end list are reserved identifiers however any
suitable nomenclature can be utilized.
In this simple embodiment of the present invention the list is then
submitted to a data compression compiler that accepts the data
field list and creates two output files. The first is a data
compression algorithm set comprised of data field specific encoders
and the second output file is a data decompression algorithm set
comprised of encoded data field specific decoders. In practice this
compiler can be implemented in any high level language, machine
code, or any variant in between. In addition the language can be
Java, r Visual Basic, or another interpreted language to be
dynamically operated over the Internet.
More advanced embodiments of the list can be created where the
order of the data fields is important to the selection of encoders.
In this case the fields are an ordered vector set and the encoders
are also an ordered vector set.
TABLE-US-00003 <start list> <list file name (optional)>
<ordered data field list 1, optional parameters> <data
field a, optional parameters; data field b, optional parameters; .
. . ; data field n, optional parameters;> [data field a
compression algorithm x, optional parameters; data field b
ompression algorithm y, optional parameters; . . . ;data field m
compression algorithm n] [data field b compression algorithm x,
optional parameters; data field a compression algorithm y, optional
parameters; . . . ;data field m compression algorithm n] <end
list>
In this more sophisticated embodiment the encoders are selected
based upon the data fields and their specific ordering.
In yet another embodiment of the present invention the sets of
ordered data fields can be assigned to sets by set name, giving the
ability for nesting of sets to facilitate ease of coding.
In yet another embodiment of the present invention the optional
parameters to each encoder are utilized to share parameters amongst
the same or different data fields.
Although illustrative embodiments have been described herein with
reference to the accompanying drawings, it is to be understood that
the present invention is not limited to those precise embodiments,
and that various other changes and modifications may be affected
therein by one skilled in the art without departing from the scope
or spirit of the invention. All such changes and modifications are
intended to be included within the scope of the invention as
defined by the appended claims.
* * * * *
References